Practical and theoretical advances in predicting the function of a protein by its phylogenetic distribution

Abstract

The gap between the amount of genome information released by genome sequencing projects and our knowledge about the proteins' functions is rapidly increasing. To fill this gap, various 'genomic-context' methods have been proposed that exploit sequenced genomes to predict the functions of the encoded proteins. One class of methods, phylogenetic profiling, predicts protein function by correlating the phylogenetic distribution of genes with that of other genes or phenotypic characteristics. The functions of a number of proteins, including ones of medical relevance, have thus been predicted and subsequently confirmed experimentally. Additionally, various approaches to measure the similarity of phylogenetic profiles and to account for the phylogenetic bias in the data have been proposed. We review the successful applications of phylogenetic profiling and analyse the performance of various profile similarity measures with a set of one microsporidial and 25 fungal genomes. In the fungi, phylogenetic profiling yields high-confidence predictions for the highest and only the highest scoring gene pairs illustrating both the power and the limitations of the approach. Both practical examples and theoretical considerations suggest that in order to get a reliable and specific picture of a protein's function, results from phylogenetic profiling have to be combined with other sources of evidence.

Extensive complementarity between gene function prediction methods

“…First, the phyletic profiles (PP) method represents the COG/NOG gene families (OGs; see below) by the presence/absence patterns of their member genes across 2,071 genomes, and then makes inferences about gene functions by comparing such patterns via pairwise similarity ( Fig. S1a; Pellegrini et al, 1999;Kensche et al, 2008;de Vienne and Azé, 2012) or by machine learning (Tian et al, 2008;Škunca et al, 2013). Second, biophysical and protein sequence properties (BPS) method includes 1,170 features representing amino acid composition, particular motifs or periodicities (King et al, 2001;Jensen et al, 2003;Lanckriet et al, 2004;Minneci et al, 2013) and various sequence statistics (summary in Sec.…”

Expectation-maximization approach to Boolean factor analysis

“…Thus, revealing sets of proteins coherently appeared in different organisms may facilitate the search of functional modules in genome structure. Recently there were many attempts to reveal modular structure in genome data sets using different blind statistical methods, such as cluster analysis, independent component analysis and others (see [19] for review). Since the concept of genome functional modularity is completely compatible with BFA generative model described here, it was a challenge for us to apply BFA based methods to reveal hidden factor structure in some large genome data set.…”

A Survey on Evolutionary Analysis in PPI Networks

“…Galperin & Koonin, 2000;Moyle et al, 1994). Coevolution of proteins may be assessed at sequence level (sequence co-evolution) by correlating evolutionary rates (Clark et al, 2011), or at gene family level (gene family evolution) by correlating occurrence vectors (Kensche et al, 2008). An occurrence vector or a phylogenetic profile (phyletic pattern) (Tatusov et al, 1997) is an encoding of protein's (homologue's) presence or absence within a given set of species of interest (Kensche et al, 2008).…”

“…Coevolution of proteins may be assessed at sequence level (sequence co-evolution) by correlating evolutionary rates (Clark et al, 2011), or at gene family level (gene family evolution) by correlating occurrence vectors (Kensche et al, 2008). An occurrence vector or a phylogenetic profile (phyletic pattern) (Tatusov et al, 1997) is an encoding of protein's (homologue's) presence or absence within a given set of species of interest (Kensche et al, 2008). In general, the methods for correlating protein evolution have been successfully applied to predict a physical or functional interaction between proteins (Clark et al, 2011;Kensche et al, 2008), where sequence co-evolution is powerful in predicting the physical interaction and phylogenetic profiling is a good indicator of functional interplay between proteins in a broader sense.…”

“…An occurrence vector or a phylogenetic profile (phyletic pattern) (Tatusov et al, 1997) is an encoding of protein's (homologue's) presence or absence within a given set of species of interest (Kensche et al, 2008). In general, the methods for correlating protein evolution have been successfully applied to predict a physical or functional interaction between proteins (Clark et al, 2011;Kensche et al, 2008), where sequence co-evolution is powerful in predicting the physical interaction and phylogenetic profiling is a good indicator of functional interplay between proteins in a broader sense. Large-scale co-evolutionary maps have also been constructed and analysed for better understanding the evolution of a species and its link to protein interactions (see e.g.…”